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Abstract:

Embodiments provide for the creation of network pages or presentations
that include pages on which programmatically selected/categorized content
and manually-identified content from website users may be combined and
displayed.

Claims:

1. A method for providing content on a website, the method comprising:
assigning at least one of a plurality of categories to at least a portion
of a network page; and displaying content generated from multiple content
items on the network page; wherein displaying content includes
programmatically determining the one or more of the multiple content
items are pertinent to the category of the assigned network page by
programmatically analyzing a text content of each of the one or more of
the multiple content items; and wherein displaying content includes
accepting a submission by an editorial class user who has identified the
submission as being pertinent to the category.

[0004] All of the aforementioned priority applications are hereby
incorporated by reference in their entirety for all purposes.

TECHNICAL FIELD

[0005] The disclosed embodiments relate generally to the field of content
provided on network sites. More particularly, the disclosed embodiments
relate to a system and method for aggregating and displaying content on
an online site.

BACKGROUND

[0006] With the growth of the Internet, web-sites are increasingly
providing content such as news, articles, and stories. There are an
increasing number of sources for content on the Internet. With this
growth, content distribution on the Internet has become disorganized. For
example, popular news sites carry redundant news items, so users have
little need to visit more than one news source. For a user to receive
comprehensive news items of a given topic, such as their local area, the
user may have to visit numerous sites and materials. At the same time, a
user may find it difficult to find a news item about an obscure category,
such as a disease or a hobby. In such cases, users often rely on search
sites, such as provided by YAHOO! or GOOGLE to locate content items of
interest.

[0007] There are web-sites that categorize content for users, but in most
cases, the categories are fairly broad and non-specific. For example, the
typical news site will provide aggregation of news stories under headings
such as World News, U.S. News, Sports, Business etc. The aggregation and
categorization of such stories is typically done through some manual
intervention. A typical situation is that the story is categorized in a
general category at its origin, and then distributed for consumption or
display on multiple web-sites. Another situation is that editors provide
keywords in a story, or associate the keywords with the stories, so that
when someone types a search term at a search site that matches the key
word, the story will be presented in the search result.

[0008] Some sites provide category-specific content by searching for
content that matches a particular search term. Such sites typically rely
on the use of search terms to ensure that a particular content item is
sufficiently pertinent to a particular category. When content is
identified, it is known to belong to a category of the search term.

BRIEF DESCRIPTION OF THE DRAWINGS

[0009]FIG. 1 illustrates a system for retrieving, categorizing and
aggregating content for display on a network, according to an embodiment.

[0010]FIG. 2 illustrates a basic method for automatically analyzing
content items for categorical content, according to an embodiment.

[0011]FIG. 3 illustrates a method in which categorization of content
items is performed in order to aggregate and display content on network
pages corresponding to one or more categories, according to an
embodiment.

[0012]FIG. 4 is a method illustrating automated retrieval,
categorization, aggregation and display of content items, according to an
embodiment.

[0013]FIG. 5 illustrates processes that form part of a programmatic
analysis to categorize content items based on the item's text, according
to an embodiment.

[0014]FIG. 6 is a block diagram of a system that produces formatted
network pages where content is aggregated based on categories, according
to an embodiment.

[0015]FIG. 7 illustrates a method in which content from a second category
is suggested on a formatted page where content is aggregated and
displayed for a first category.

[0016]FIG. 8 illustrates a formatted page for displaying content that is
derived from categorized content items, according to an embodiment.

[0017]FIG. 9 displays a formatted page, according to another embodiment.

[0018]FIG. 10 illustrates a method for categorizing content based on
geographic information, under an embodiment of the invention.

[0019]FIG. 11 illustrates a system for providing categorized content that
is both programmatically and user-identified on a page or presentation of
a website, according to one or more embodiments of the invention.

[0020]FIG. 12 illustrates a process for enabling editorial class selected
content to be displayed in connection with programmatically identified
content, under an embodiment of the invention.

[0021]FIG. 13 illustrates a tool for facilitating an editorial class of
users in editing content items for submission on a site, under an
embodiment of the invention.

[0022]FIG. 14 illustrates a presentation provided by a user-interface of
a tool such as described with an embodiment of FIG. 13.

[0023]FIG. 15 illustrates a network page of content, containing both
editorial class user submitted content and programmatically identified
content, according to an embodiment such as described with FIG. 11 thru
FIG. 14.

[0024] In the drawings, the same reference numbers identify identical or
substantially similar elements or acts. To easily identify the discussion
of any particular element or act, the most significant digit or digits in
a reference number refer to the Figure number in which that element is
first introduced. Any modifications necessary to the Figures can be
readily made by one skilled in the relevant art based on the detailed
description provided herein.

DETAILED DESCRIPTION

Overview

[0025] Embodiments of the invention describe a system and method for
automatically retrieving, categorizing and displaying content from a
network. An embodiment of the invention enables category-specific content
to appear together at one site or location on a network. One result that
may be achieved is that a user may access and browse the site or location
where category-specific content is aggregated and updated.

[0026] In one application, a web page is provided that can be browsed by a
user, where the web page includes content dedicated to a particular
category. The content may include links to articles, news stories and
other content items that are about the particular category. For example,
the user can view a web page having updated news stories about a
particular hobby, disease, person of interest or company. These articles
and news stories may be retrieved from various other network sources, and
presented on the page to maximize interest and reduce redundancy. As
such, the user is provided with an alternative to having to submit search
queries in order to view category-specific content items.

[0027] In an embodiment, a large number of content items may be retrieved
and categorized into an even larger number of categories through
programmatic implementations. This allows for content to be generated for
various category-specific web pages (or portions thereof). The content
for each page may be retrieved automatically from various network sites.

[0028] One embodiment provides an automated process where content is
categorized, aggregated and selected for display on category specific
pages. This enables the creation of category-specific web pages that
provide fresh and pertinent content for a specific category. Readers
interested in a particular category may view a web page as a single
source where information about the category of interest is provided. An
embodiment such as described may obtain content for such pages from
numerous sources that most users would not have time to access manually.
The user may not even have knowledge of all the different sources that
provide content about that particular category at a given moment.

[0029] According to an embodiment, a plurality of content items are
retrieved from multiple network sites. Content from each content item is
programmatically analyzed in order to associate that content item with
one or more categories. The one or more categories may be part of a
larger set of predefined categories. A network page is assigned to one or
more corresponding categories in the set of predefined categories. At
least some content is provided on the network page using one or more
content items that were associated with the one or more categories
assigned to that network page.

[0030] Examples of content items include news items and events,
announcements, messages, press releases, product and pricing
advertisements (or other information), sale information (e.g. department
store sale), pricing events, and articles. In one embodiment, content
items include text segments that can be used to perform analysis
operations described herein. The term "content" may refer to
reproductions or derivations of content items, summaries, segments or
portions of content items, and/or links to other network sites where the
content items are provided.

[0031] Embodiments of the invention categorize content items into a
selected set of categories. The selected set of categories are from a
much larger number of possible categories. In one embodiment, the total
number of possible categories in which news items pertain to is of the
order of 103 or greater. A category may be broad, such as a genre
(entertainment, business, news items), or specific (individual
celebrities, professional athletes, companies). Categories are
identifiable by sub-categories (e.g. entertainment is defined by
individual celebrities and movie titles) and/or by key words, phrases, or
text-strings. However, as will be described herein, the occurrence of a
key word, phrase or text-strings that is a category identifier may only
trigger a determination as to whether a particular content item
containing that identifier should be associated with the category
identified by that identifier.

[0032] An embodiment of the invention may be implemented on or with a
network such as the Internet. For example, content items may correspond
to news stories, articles and other documents made available at any one
of the plethora of web-sites where news and other content is provided.

[0033] The term "programmatically" means an automated step, or
substantially automated process performed through use of
computer-executable instructions, such as by processors which execute
instructions in the form of programming code.

[0034] As used herein, the term "module" includes a program, a subroutine,
a portion of a program, a software component, firmware, a hardware
component, or a combination thereof, capable of performing a stated task
or function. A module can exist on a single machine, or be distributed to
more than one machine.

[0035] Embodiments described herein may include instructions that are
carried on or executed by a computer-readable medium. As used herein, a
computer-readable medium may include any machine or device having
resources to execute, store, or otherwise carry instructions for
performing operations and steps of embodiments described herein. Modules
and software components described herein may be executed on one or more
machines and by one or more devices. Instructions for executing modules
and software components may be carried in memory mediums, either
internally or externally from machines on which instructions are
executed.

[0036] According to another embodiment, a method is provided in which a
plurality of content items are retrieved from one or more network sites.
Content for each of the plurality of content items is analyzed in order
to associate that content item with one or more categories in a larger
set of categories.

[0037] System Overview

[0038]FIG. 1 illustrates a system for retrieving, categorizing and
aggregating content for display on a network, according to an embodiment.
The system may be comprised of a combination of modules or components
that cooperate with one another. A system such as described automates the
acts of retrieving and sorting content items into categories through the
user of a combination that includes a crawler 110, a categorizer 120, and
a knowledge database 130. The system may aggregate or select content for
display based in part on the retrieved content through the use of a
bucket 140 and an editor 150. The system may operate on a network such as
the Internet.

[0039] A system such as described in FIG. 1 may be used to maintain
numerous pages, and each of the pages may include categorized content
that is aggregated and maintained in an updated state. Each page or
document may display aggregated content from various network sites based
on one or more specific categories assigned to that page. Each page may
be routinely and automatically updated using additional content
aggregated from any one of the numerous web sites that the system
accesses. In one embodiment, the pages on which the system maintains and
provides content are made available to users over the Internet.

[0040] Crawler 110 may be configured to visit pre-determined network sites
where news stories and other content are periodically provided. For
example, newspaper cites and cites that carry wire services for major
news organizations such as REUTERS, ASSOCIATED PRESS, NEW YORK TIMES, and
BLOOMBERG may be periodically accessed. In addition, crawler 110 may
access local (geographic specific) news resources, journals, real-time
information providers (stock quotes from stock exchanges), web clippings,
message boards, online retail sites (including sites where pricing
information for "brick and mortar" outlets are provided), or any other
site where content is provided and updated on occasions. Crawler 110 may
be configured to automatically provide registration information from
sites that require users to be registered. For example, crawler 110 may
enter login, password, or otherwise perform a script in order to gain
access to a web-site. In addition, crawler 110 may be configured to visit
individual sites at particular times, or at designated frequency
intervals. For example, crawler 110 may be programmed to visit different
network sites at different intervals based on how frequently different
web sites are known to refresh their own content.

[0041] In an embodiment, crawler 110 provides text-based content to
categorizer 120. Categorizer 120 works with knowledge database 130 to
categorize content provided by crawler 110. In particular, categorizer
120 and knowledge database 130 may combine to determine one or more
matching categories for a particular content item. In an embodiment,
categorizer 120 uses multi-dimension or multi-space algorithms in order
to sort specific content items into one or more of the categories defined
in the knowledge database 130. Categorizer 120 may analyze text from the
content items in order to find text-string combinations which match
specific category definitions. Knowledge database 130 may store category
definitions (described in more detail with as nodes in FIG. 5) which
consist of a set of text-string combinations that are identifiers of a
particular category. Identifiers may be of different degrees. Some
identifiers may be used to increase confidence, others to be more
determinative. A more detailed explanation of how a category identifier
is used is provided with FIG. 5.

[0042] A category identifier may be either one of a required or pertinent
set of text-string combinations. As will be described, one embodiment
provides that the presence of one or more words, phrases, names or other
text-strings from the required set of a given category definition
triggers the system into considering that category as a candidate
category that matches the content item. The presence of additional
identifiers, whether from the required or pertinent set, may be
considered in a subsequent determination of whether the given category is
a good match for the content item.

[0043] Thus, the occurrence of a single text-string that corresponds to a
category identifier is, by itself, often insufficient to match the
content item of the text-string to the category of the identifier.
Rather, the presence of the identifier in the content item marks a
candidate category that is subsequently analyzed. Additional analysis is
done on the content item. According to one embodiment, for any given
candidate category, the additional analysis factors in the following: the
number of identifiers (required and pertinent) in the content item, the
commonality of the identifiers that are present, the placement of the
identifiers in the content item, the relation of the identifiers with
surrounding text, the character length of the identifiers, and a general
measurement of how well individual identifiers identify a category based
on the size of the category definition and other factors. Other factors
may also be used.

[0044] In one embodiment, knowledge database 130 contains a large number
of nodes, alternatively referred to as category identifiers. In one
application, the total number of nodes that can be maintained may exceed
the order of 103. For example, in one specific application, the
number of nodes maintained by the knowledge database is of the order of
106. A system such as described herein is capable of retrieving
content items from various sources and categorizing content from the
content items into any one of the plethora of categories. One application
for such an embodiment is a web-site that provides thousands, or tens of
thousands (or more), of internal web-pages, each specific to one
category, or alternatively to a small set of categories. In such an
application, each internal web page is a site where category-specific
content is aggregated, and possibly selected for display.

[0045] Past attempts to aggregate and categorize content for display on
network sites have focused on using a combination of manual editing,
and/or key word queries to locate, categorize and select content for
display. Such attempts have been limited in their ability to categorize
data into anything but a small set of categories. For example, many news
sites that pull news from other web sites, display news items in broad
categories, such as World News. Sports, Health, Business etc. In contrast
to such systems, embodiments described herein can, for example, host one
page for each publicly traded company in a general Business category, and
on each company-specific page, news items for that company are frequently
retrieved and displayed. This gives the user the ability to view fresh
news items for one company at one site, rather than making the user sift
through a broader general category for news that may or may not be of
interest. Websites such as google.com provide the user with the option of
searching news items based on a keyword query. However, such sites
provide only search results for a user's query. The user still has to
sift through the search results, which may or may not be of interest.
There may have been problems with the user's search (such as one of the
keywords having two different meanings). Furthermore, the search results
only locate stories with given keywords, the search results make no
determination as to whether the story is likely to be of interest. In
contrast, embodiments described herein enable generation of web pages
where content is category-specific and likely to be of interest to
someone who is interested in category of the web page.

[0046] Crawler 110 may retrieve thousands of items, such as articles and
news stories, in a given interval of time (such as a day) using a large
number of sources (such as web-sites where articles are published). Next,
categorizer 120 scans text content from the content items in order
determine candidate categories. As stated, candidate categories may refer
to each category that has an identifier in the text content of the item.
In one application, the scan of a given item yields tens or hundreds of
candidate categories. Categorizer 120 makes a determination from the
candidate categories as to which categories are most appropriate for a
given content item using the algorithms (such as multi-dimensional
processes described with FIG. 5).

[0047] In determining what category matches a particular content item,
categorizer 120 may make the following determinations, either absolutely
or in terms of probabilities: (1) associate a text-string with a
candidate category; (2) determine whether the text string is in fact
referring to the candidate category; and (3) if the text string is
determined to refer to the candidate category, determine if the candidate
category the subject of the content in the content item (i.e. is the
article about the candidate category?).

[0048] Knowledge database 130 may include information for use in analyzing
the applicability of a category identifier to a particular category. In
one embodiment, knowledge database 130 includes information for enabling
the categorizer 120 to make the first two determinations of the preceding
paragraph. Specifically, knowledge database 130 may correlate
text-strings with categories, and also provide information in order to
determine whether the occurrence of the text-string implies the content
item is in fact referring to the correlated category.

[0049] The information maintained by knowledge database 130 may include
information that indicates the commonality (or inversely the uniqueness)
of particular category identifiers. Commonality and uniqueness are
factors which influence the confidence that the presence of a particular
category identifier in the text of a content item in fact means that the
content item is about the category of that category identifier. For
example, knowledge database 130 may contain information from the British
National Corpus on how common (or unique) a particular word or phrase is.
Similarly, the United States Census Bureau publishes the 5000 most common
first names, and the 35000 most common surnames. The commonality of
geographic places, such as city and street names, may be obtained from
sources such as RAND MCNALLY.

[0050] To provide one example, the appearance of text string "Bill Gates"
may identify MICROSOFT and BILL GATES as candidate categories. But
knowledge database 130 will also factor in the possibility that "Bill
Gates" may mean a different person, based on the U.S. Census Bureau
information indicting Bill and Gates are semi-common first names and
surnames. If the same article includes the word "windows", the
commonality of that word may be determined by the British National
Corpus. Thus, knowledge database 130 may determine the likelihood that
the article is referring to BILL GATES and MICROSOFT based on the
commonality of the name and of the word "windows". Information for
determining commonality/uniqueness of words, names and phrases may enable
categorizer to determine a likelihood that "Bill Gates of Topeka, Kans.
was standing by his window when he saw his neighbor's house burning," is
not a story about Bill Gates, founder of Microsoft.

[0051] It should be noted that even if occurrence of "Bill Gates" and
"window" is deemed to be a likely reference to the more famous founder of
MICROSOFT, additional analysis is performed to determine if the article
is in fact about MICROSOFT or the famous founder of that company. For
example, categorizer 120 may be configured to decipher that a story line
"After winning the lottery, John Smith may just as will be Bill Gates
when he invented Windows," is a story that is not about the founder of
MICROSOFT. A more detailed description of how such determinations are
made is provided with embodiments described below.

[0052] Categorizer 130 outputs categorized content. Categorized content
includes content from items that have been categorized into one or more
categories. In one embodiment, text from a content item is outputted and
assigned to a small set of categories.

[0053] Bucket 140 groups categorized content. In one embodiment,
categorized content for each category is aggregated as it becomes
available. The output of bucket 140 includes content clusters, which
refers to a set of aggregated content for individual categories. The
aggregated content may include text from the original content item. In
addition, graphics, such as images, may be stored with the text content
from the item. Some or all of the text from a particular content item may
form the content from that item that is part of the set of aggregated
content. It is also possible for the image or graphics originally
provided with the content item to form part of the content from that
item, and as such, be part of the set of aggregated content.

[0054] Aggregated content for each category is provided to a module
referred as editor 150. Editor 150 selects which of the aggregated
content is to be displayed at a given interval on a corresponding network
page of that category. Editor 150 performs operations for generating
displayed content from the aggregated content clusters. Editor 150
selects what content is to appear on a network page using a set of
selection criteria or rules. According to one embodiment, bucket 140 uses
content analysis of each item forming the aggregated content to determine
when items in the aggregated content are the same, or at least very
similar. Editor 150 selects content items from the bucket 140. One
criteria that may be used by editor 150 to select items from the
aggregate content is to exclude redundant content items from appearing on
the page. For example, if two stories in the aggregated content each
contain an identical portion, the editor 150 may determine that only one
of the two stories needs to appear on the page. Another rule or factor
that may be used to select a particular content item from the aggregated
content is the source of the content item. For example, some web sites
may be preferred over other web sites as sources of news stories. Other
examples of factors that can be used in selecting what content to display
from aggregated content items include key words or phrases and freshness.
Additional factors that may be used include, location/source of content
items, location of subject of content items, prominence of content item,
geographic distance between a subject of the content item and the
location of the readers, and geographic distance between subject of
content item and source of content item.

[0055] In one embodiment, aggregated content may individually be presented
in the form of short summaries, headlines, and sub-headlines, with links
to the entire content item. The link may be to the network site where the
content item was originally retrieved from and analyzed.

[0056] Methodology

[0057] FIGS. 2-6 illustrate methods, according to embodiments of the
invention. Embodiments such as described in FIGS. 2-6 may be performed
though use of machines that can execute instructions stored on
computer-readable mediums. Specifically, methods such as described in
FIGS. 2-6 may be performed by one or more processors, which execute
instructions for performing steps or operations of the methods described.
A system such as described in FIG. 1 is an example of a suitable system
for performing methods such as described below. Any reference to an
element of FIG. 1 is made solely for illustrative purposes.

[0058]FIG. 2 illustrates a basic method for automatically analyzing
content items for categorical content. As described, a step 210 provides
that content is retrieved from different network sites. For example,
content may be retrieved from different web-sites using a crawler 100.
Examples of network sites that can be used to retrieve content items
includes web-sites where articles such as news stories are provided.
Other examples include sites where press releases, product listings,
advertisements, events and other news worthy or content of interest items
are provided.

[0059] Step 220 provides that content items are programmatically analyzed
in order to determine which one of a predefined set of categories belong
to that item. For example, this step may be performed by categorizer 130
using knowledge database 120 to analyze text from a news story. The
knowledge database 130 may contain information for defining a large
number of categories. The text from the news story may be automatically
scanned for text strings that identify candidate categories. A series of
analysis tools may be used to determine which candidate categories are
potentially related to the content item.

[0060] In step 230, the analysis performed in step 220 is used to sort the
items retrieved in step 210 into one or more of the predefined
categories. In one embodiment, the category or categories that are
assigned to the content item are selected from the candidate categories.
For example, one news article may generate hundreds of candidate
categories. Of the candidates, a programmatic determination is made to
determine which categories are most appropriate for a given content item.
The content item is assigned to one or more categories that are deemed
appropriate based on criteria and ruled for determining which candidate
categories are most relevant or accurate in identifying the best category
for a particular content item.

[0061]FIG. 3 illustrates a method in which categorization of content
items is performed in order to aggregate and display content on network
pages corresponding to one or more categories, according to one
embodiment of the invention.

[0062] Step 310 provides that content items, such as articles, news
stories etc, are retrieved from different web sites (assuming use of a
network such as the Internet).

[0063] In step 320, the content items are scanned in order to identify
category identifiers. In one embodiment, text content of the content
items is scanned. An attempt is made to find as many category identifiers
as possible in the text content.

[0064] Step 330 provides that an analysis is performed of the category
identifiers identified from the scan of the content item. A more detailed
discussion of the analysis performed on the category identifiers is
provided with FIG. 5. The analysis is performed to identify which
categories should be assumed as being most relevant to the particular
content item.

[0065] In step 340, an aggregation of content items is made available for
a particular category. The aggregation may be made available visually on
a page that is accessible to others over a network (such as the
Internet). The aggregation of the content items may be in the form of a
summaries or edited versions of the content items appearing on the page
together. Links to network sites where the content items are actually
provided may also be included as, or part of, the aggregated content.

[0066]FIG. 4 is a method illustrating automated retrieval,
categorization, aggregation and display of content items. In step 410,
categories are defined by one or more identifiers. A category definition
may include a set of names, words, phrases, geographic locations or other
text strings. For example, the category definition for a celebrity may
include the celebrity first name, last name, nickname, film biography,
and possibly the place of residence or birth for the celebrity. The
category definition for a location may include the name of the place, the
name of geographic identifiers of the location, longitude and latitude of
the location, historical names and nicknames for the location, the names
of parks, bodies of water, tunnels, rivers, schools jails, businesses
(restaurants etc), and any other information that is indicative of that
location.

[0067] In step 420, articles (or other content items) are automatically
retrieved from multiple network sites. For example as discussed with
other embodiments, web sites where news items, articles, messages etc.
may be routinely accessed, and content appearing thereon may be
retrieved.

[0068] Step 430 provides that the content of the articles are scanned, or
otherwise inspected for identifiers of categories in order to identify
candidate categories. In one embodiment, text is scanned for names,
words, phrases, geographic locations and other text strings that
correspond to identifiers of categories. A candidate category means that
an identifier of that category appears in the article, but other analysis
needs to be performed in order to be able to conclude that the article
belongs in that category.

[0069] In step 440, an analysis is done to determine which candidate
category or categories is a suitable categorical match for the particular
article. A more detailed explanation of the process for performing the
analysis is described with FIG. 5. The result of performing the analysis
of this step is that the article is assigned to one or more categories.

[0070] In step 450, articles matching a particular category are
aggregated. In the case where a category is specific (such as a specific
celebrity or athlete), the rate at which articles are accumulated may be
relatively slow. For categories that match genre's (such as entertainment
and sports), the rate of accumulation may be fairly quick. In many cases,
there may be too many articles to be displayed on one screen or network
page.

[0071] In step 460, articles from the set of aggregated articles are
selected to be displayed or otherwise rendered in a medium that is
specific to the category of the articles. This step may be performed in
order to select what articles are made available on a network page,
placement of articles or links to articles on a page, and what portion or
even information is displayed about selected articles on the page. The
selection process may be based on several factors. In one embodiment,
these factors include (1) how recent article was published, (2) amount of
interest in the article from the public (information may be obtained from
the source or from the subject matter or identifiers in the article) (3)
the degree to which a particular article varies from other articles that
have been aggregated for the network page (e.g. does the article share
the same identifiers as other articles for the same category), (4) the
degree of confidence that exists in the determination that the article
belongs in the category, (5) how geographically close the content items
are to the subject of the content items; (6) the geographic distance
between a location of the content item and a location of the reader, (7)
prominence of the source of the content items (e.g. national newspaper),
and (8) how often the source of the content item reports about a
particular subject. With respect to (8), an example is a publication that
is authoritative for a particular topic. For example, an automotive
racing magazine is more authoritative about a race car driver or racing
story than a local news paper. Therefore, in the example provided, one
embodiment may provide more weight to news stories identified as belong
to an automobile racing category when the news stories originates from
the more authoritative source (the magazine).

[0072] Categorization

[0073] As described above, embodiments of the invention provide for
automatic categorization of content retrieved from different network
sites. In one embodiment, text content in different articles is retrieved
and scanned for category identifiers, which may be in the form of words,
phrases, names or locations. For each category identifier in a given
article, additional analysis is performed in order to determine whether
an article is about or otherwise belongs in a category.

[0074]FIG. 5 illustrates a programmatic analysis performed on text
content 510. An analysis such as described herein may be performed by a
system such as described in FIG. 1. Reference to elements of FIG. 1 are
made for illustrative purposes only. In an embodiment, text content 510
corresponds to content that is read from an article on a network site.
The results of the overall analysis is a determination of an appropriate
category for the text content 510. FIG. 5 shows results of several
independent processes performed as part of the overall analysis for
assigning the article to a category. Each category may be represented by
a node. A node may defined by a set of identifiers, which include words,
phrases, names and other text-strings. In one embodiment, each node
includes, as identifiers, one or more of (i) required term(s) and (ii)
pertinent term(s). A required term may correspond to a category
identifier that is fairly unique to a particular category. The existence
of a required term in text invokes the category of that required term as
a candidate. In one embodiment, a node may have one or more (even several
or hundreds) of required terms. One embodiment provides for the node to
be a candidate for a particular category, at least one of the required
terms has to be present in the text content.

[0075] For example, the full name of a celebrity, together in one text
string, is an example of a required term for that celebrity. A common
nickname used to identify that celebrity (e.g. "Madonna" or "Prince") may
also correspond to a required term for a celebrity. The pertinent term is
a term that is more common to multiple nodes. For example, the term
"Corvette" may be a pertinent term for the artist "Prince", and
specifically to a song by the artist, but "Corvette" itself could be a
reference to car model. Thus, support terms are used to build confidence
that the candidate node is actually being referenced, and even is the
subject matter of the text content.

[0076] According to one embodiment, the existence of required terms and
support terms is used to quantify a likelihood that (i) a given article
is in fact referencing the category of the node, and (2) the category
being referenced is a subject of the article, so much so that the article
should be assigned to that category. A more detailed description of the
quantitative analysis is provided below.

[0077] According to one embodiment, knowledge database 130 may store node
definitions, including required terms and support terms for each node.
The categorizer 120 may perform individual processes of the overall
analysis in determining when a node matches an article. The determination
that a node matches an article may be made automatically, through
programmatic means, such through instructions executed by categorizer
120.

[0078] A node may be invoked as a candidate if one of the required terms
for that node appears in the text content. Thus, each candidate node in
column 514 has at least one required term from text content 510. The
column 546 lists at least one of the required terms that appear in the
text content 510 for a candidate node. For example, in column 546, the
phrase "Patent and Trademark Office" is an identifier (a required term)
for the node "law/patent-trademark". To further the example, the presence
of the name "Lee" is a required term for the node "city/durham-nh" and
"city/lee-fl".

[0079] A column 514 lists nodes by name or node identification. Prior to
completion of the analysis, all listed nodes are candidates. In the
example provided, only one node is a matching node for the particular
text item. This node is indicated in a separate row 540. Various
parameters are determined about each mode in order to determine whether a
particular node is a matching node for the particular text item. A column
512 lists a binary parameter that is assigned a value based on a
determination of whether the category of that row is a subject of that
article. For this parameters, the value of "1" indicates that node is a
subject of the article (alternatively phrased, the article is about the
category of the node). The value "0" indicates that the article is not
about the category of the node. For the node to be a matching node, the
value of the column 512 would need to indicate that the article is
sufficiently about the category of the node to warrant a positive value.
The determination of the value of column 512 may be made based on the
value of the other parameters.

[0080] Column 516 lists a Fail Parameter for each candidate node. The Fail
Parameter is a Boolean determination as to whether the candidate node is
actually being referenced. It indicates whether reference to the required
term of a given candidate node is an accurate semantic reference. For
example, in the example provided, "Stephen, MN" is being referenced as a
city because the article quotes a person named "Stephen". Even though
"Stephen" is a required term for "Stephen, MN", the article is not
actually referencing the town. Thus, the node "Stephen, MN" is assigned a
negative Fail Parameter, as the reference to the required term of that
node is not accurate.

[0081] The determination of Fail Parameter is based on a commonality
determination. Factors that affect the commonality determination include
the commonality/uniqueness of the required term, as well the length of
the string for the required term. Short and common required terms
indicate a negative Fail Parameter, while, long and unique strings
indicate a positive value. In the example, "Patent and Trademark Office"
is an example of both a long and unique string, while the string "Lee" is
an example of a short, non-unique identifier that yields a negative
result. A positive Fail Parameter result increases the confidence that a
node is a matching node.

[0082] Column 518 lists the Score Parameter for each candidate node. The
Score Parameter is another confidence rating that the reference to the
required term is semantically accurate. This Score Parameter may be based
on commonality of the required term, as well as other factors.

[0083] Column 520 and 522 provide Group Hits and Total Hits parameters.
Each required term may be part of a group of terms that are equivalent in
semantics, but different in syntax. For example, the locations "Mt.
Lebanon. Penn." and "Mount Lebanon, PA" are semantically equivalent
references to the same city. The parameter Group Hits measures the number
of hits an entire group of required terms receives. Depending on use and
learning algorithms, there may be a difference between 3 hits to one
group, and 3 hits to three groups. The Total Hits parameter measures how
many total hits of identifiers (required terms and supplemental terms)
are in the text content 510 for a given candidate node.

[0084] Column 524 lists the parameter "Number of Occurrences" for each
candidate node. The Number of Occurrences counts the number of times the
required terms of the candidate node appear in the text content 510.

[0085] Column 526 lists the parameter "Position". The Position parameter
is a measurement of proximity between the start of the article and the
first required term of the candidate node. Confidence is increased when a
required term is close to the start of the article. One exception is that
a geographic node may contain a required term at or near the end of the
article.

[0086] Column 528 is a Boolean parameter "BadState". The BadState
parameter is an indication that there is a bias towards a candidate not
being a matching node, where the indication is based on geographical data
in the content item.

[0087] Column 530 indicates a value for the parameter "Node Size". This
parameter is a measurement of the number of required terms and pertinent
terms in a particular geographic node. In the event that two geographic
nodes are equally suitable matching nodes for a given article, this
parameter assumes the node with the most required terms is the more
popular, and thus more likely the subject of the given article. For
example, "New York City" may have numerous required terms and pertinent
terms, including "York", "Big Apple" and "Empire State Building". The
Node Size parameter may be used to distinguish an article as being about
or more pertinent to New York City, as opposed to York, Pa.

[0088] Column 532 lists the parameter "Words". This is a count of the
number of words for the required term of the candidate node that appears
in the text content 510.

[0089] Column 534 lists the parameter "Length". This is a count of the
number of characters for the required term of the candidate node that
appears in the text content 510. With both the Words and Length
parameters, the greater the value, the more unique the required term that
appears in the article. Consequently, the greater the value of the Fail
parameter, and the more likely that the candidate node is a matching
node.

[0090] Column 536 lists the parameter "Post". This parameter measures the
number of nodes in the knowledge database 130 which list the required
term as part of a longer string of characters as a required term. For
example, the required term "San" will produce a large value because of
various cities and streets that start with the three letters. The higher
the value, the less likely the candidate node is a matching node.

[0091] The column 538 provides the parameter "Node". This node is similar
to column 536, in that it measures the number of nodes that contain the
required term of that candidate node. As with the Post parameter, the
greater this value, the less likely that the candidate node is a matching
node.

[0092] The column 540 lists the parameter "Frequency". It measures the
number of times that the required term appears as any part of any
identifier for any node.

[0093] The column 542 provides the parameter "Short". The Short Parameter
indicates a probability that the required term of the candidate node
appears in the text content as part of a proper noun. Words immediately
before and after each required term may be inspected for capitalization
in determining this Boolean value. For example, if the required term is
capitalized, not at the beginning of a sentence, and preceded or followed
by another capital letter, the Short Parameter may indicate that the
required term is part of a proper noun. For example, in the example
provided, "Stephen" is shown as a proper noun, as it is followed by
"Kunin".

[0094] Column 544 lists the parameter "Multi". This parameter is a
combination value of one or more preceding values. For example, it may be
a summation or average of two or more preceding parameters. The lower
this number, the more likely that the candidate node is a matching node.

[0095] The parameters in columns 532-544 indicate processes performed on
required terms of candidate nodes. The same processes indicated by the
parameters in columns 532-544 may be performed on support terms of each
candidate node. That is, text content 510 may be scanned for support
terms of each candidate node. For identified support terms, the Word
Parameter, Length Parameter, Post Parameter etc. are determined. In
general, analysis for support terms provide confidence for a candidate
term, but are not determinative.

[0096] A learning algorithm may be implemented in order to train a system
to use the various parameters to match categories to articles. The system
may be trained to weight parameters, determine overall scores, and draw
conclusions for determining when candidate nodes are matching nodes. In
one embodiment, a learning process is conducted where each matching node
of an article is manually inspected to determine whether the article and
node are a good match. When bad matches are found, a system such as
described in FIG. 1 is trained to identify a bad match when a combination
of parameters in the future yield worst values on each dimension. The
manner in which support terms influence analysis of required terms may
also be toned with experimentation and learning processes. With use of a
learning mode and implementation, a set of rules may be developed that
instructs a system on how to treat the occurrence of given values, or
conditions, when analyzing the content.

[0097] Displaying Categorized Content

[0098] According to one embodiment of the invention, categorized content
is aggregated on separate network pages, sites, or page portions, and
then made available to users over a network such as the Internet. FIG. 6
illustrates a system where aggregated content can be displayed on a
network page.

[0099]FIG. 6 is a block diagram of a system that produces formatted
network pages where aggregated content is provided based on categories.
In one application, a system manages content for a plethora of network
pages, and each of the network pages provides selected (when possible)
aggregated content for a particular category. A system such as described
in FIG. 6 may be substantially automated.

[0100]FIG. 6 illustrates a content item 604 that retrieved from a network
site, such as a web site where content is provided and updated. In an
example provided, the content item 604 is in the form of a news story,
with text content and an image. To further illustrate, the text content
may include a headline and/or by line.

[0101] A categorization process 610 performs an analysis such as described
with FIG. 5 in order to associate or assign the item 604 to a particular
category. Once the item 604 is assigned to the category, the item becomes
aggregated with other items. Thus, there may be several items that are
assigned to the same category. In many cases, there may be too many items
assigned to the same category, in that there is not enough desirable
space of time to display every article on the network page. Details for
categorizing and aggregating content items are described with previous
embodiments.

[0102] In an embodiment, once item 604 is aggregated with other items of a
common category, a selection process 620 is performed. Suring the
selection process, a determination is made as to whether the item 604
should be displayed on the network page over other items. The selection
process 620 may be performed using some or all of the criteria listed in
FIG. 4.

[0103] If item 604 is selected for display, a display process 630 is
performed in order to configure and format the item 604 for display on a
formatted network page 640. If the content item was originally displayed
on its network site with an image, display process 630 may store and
retrieve that image for display on the formatted page 640. Display
process 630 may also execute different sets of rules for formatting and
configuring content from the item 604 on to the network page 640. In one
embodiment, display process 630 may use a set of editorial rules 634 to
conform content from item 604 to standard journalism editing rules. For
example, if a person is provided in the image that is to be presented
with the text on the formatted page, the image is positioned so that the
person is facing inward. Another editorial rule (based on journalism
standards) is that a headline should not exceed ten words. Thus, if there
is a headline that exceeds this number on the original site, the display
process 630 may, through implementation of the editorial rules 634,
replace or truncate the headline. A complete list of suitable rules for
conforming to journalism standards and guidelines may be found in "The
Associated Press Stylebook and Libel Manual," Norm Goldstein, Editor.

[0104] The display process 630 may also use a set of display rules 638 to
format content from item 604. For example, the appearance, font and
portion of the content from item 604 may be determined from the set of
display rules 638. Display rules may provide how often content is updated
on certain portions of the category page. For example, with reference to
FIG. 9, content in column 910 may be updated faster than content on
column 920. Furthermore, the two columns may display content according to
different formats (e.g. size).

[0105] The result of the display process 630 is the formatted network page
640. The content appearing on the formatted page 640 may be updated
automatically continuously, or repeatedly over the course of a given time
period. Furthermore, it is possible for content appearing on the
formatted page 640 to originate from numerous sources on networks such as
the World Wide Web, because categorization, aggregation and selection of
the content items is done automatically. Without manual editing, a large
number of network sites can be checked for articles, news items etc.
pertaining to a specific category of the network page. Furthermore, the
large number of resources can be updated more rapidly. In one
application, the result is a network page that contains fresh content
pertinent to a very specific subject and from numerous sources on the
Internet.

[0106] Displaying Associated Categories with Categorized Content

[0107] The use of categorization processes to categorize and aggregate
content has several applications. Among these applications, it is
possible to indicate suggested content to the reader of a content item,
where the suggested content is independent in subject matter from the
content being viewed.

[0108] In one embodiment, the suggested content is determined from the
content of the item being viewed. FIG. 7 illustrates a method in which
content from a second category is suggested on a formatted page where
content is aggregated and displayed for a first category. In step 710, a
categorization process is performed on an article (or other content item)
where two or more matching nodes are identified and associated with the
article (see description accompanying FIG. 5).

[0109] Step 720 provides that the article is displayed on a network site
dedicated or otherwise associated with one of the categories identified
for that article. With reference to an embodiment such as described in
FIG. 6, the content may be displayed on a formatted page 640, belonging
to a first category.

[0110] Step 730 provides that one or more visual indications (such as
hyperlinks) are provided of a suggested category matching a second
matching node for the displayed article. In the case where hyperlinks are
used, the links may be to network sites where content is aggregated for
the suggested category. As an alternative, the suggested content may
yield an advertisement link, or display advertisement information.

[0111] An embodiment such as described in FIG. 7 can be implemented
through use of categorization and display processes described with
previous embodiments. Specifically, the ability to identify categories
through processes such as described in FIG. 5 enables the determination
of second categories. When content items are displayed on, for example, a
given page of a category, display process 630 (see FIG. 6) may provide
the visual indication of the second category or categories. The visual
indication may be in the form of a link, summary, suggested heading,
advertisement, or other data structure.

[0112] Formatted Pages

[0113]FIG. 8 illustrates a formatted page 800 for displaying content that
is derived from categorized content items, according to one embodiment. A
formatted page may correspond to an output from embodiments described
above, such as formatted page 640 described in FIG. 6. In addition, an
embodiment described with FIG. 8 assumes that content is derived from
articles categories through processes and methods described in previous
embodiments.

[0114] With reference to FIG. 8, a first content item 810 corresponds to a
segment of an article. The article may originate from a first network
site. Included in the first content item 810 is an image 812, and text
section 814. Selection of a heading or other link may display all of the
text provided by the original article that appeared at the first network
site. The image 812 may be stored from the article that was the source of
the content item. In one embodiment, the text segment 814 includes the
headline, sub-headline, and first few sentences of the text portion of
the original article. A first link 815 may be provided to a second page
for another category. The other category may be identified from the text
of first content item 810.

[0115] Similarly, page 800 may also display second content item 820 and
third content item 830. Second content item 820 may include second link
825 to a category identified by a categorization process performed on the
text content of that item. Likewise, third content item 830 may include
third link 835 to a third category identified by a categorization process
performed on the text content of that item.

[0116] In an embodiment such as shown, formatted network page 800 has a
uniform resource locator (URL) 805 or other address that is indicative of
the category of that page. For example, page 800 may be assigned to
"category A", and the content items 810-830 are selected by being
pertinent to that category. A portion of the URL 805 also includes the
term "category A".

[0117]FIG. 9 displays a formatted page 900 according to another
embodiment. In FIG. 9, a page of a given category (or set of categories)
is segmented, and each segment provides content through a different set
of aggregation, edit and/or display rules.

[0118] In an example provided, formatted page 900 is provided with four
columns. The page itself may be associated with a particular category,
and a URL 905 to the page may indicate that category. A title of the
category for the formatted page 902 may be provided in a prominent
position. One or more of the columns display content from content items
that were categorized and aggregated. In an example shown, a first
primary column 910 displays category specific content, identified through
a categorization process such as described above. A second primary column
displays content that may be category specific for that category (or of
another category), or non-category specific (e.g. top news). A left
column 930 may display advertisement links, and a right column displays
category links 940, although either left or right column may display
advertisement, category or combinations of links. The links, as well as
any other content appearing on the left or right column 930, 940 may be
category specific as well, or independent of any categorization process.

[0119] In one embodiment, different display configurations and/or rules
are used to display content on at least two of the columns. For example,
first primary column 910 may display news of a first category (e.g. local
news), and second primary column 920 may display news of a second
category (e.g. national and world news). One of the columns may be
refreshed using an automated categorization process, such as described
above. For example, a system such as described in FIG. 1 may be used to
identify, aggregate and select content for that column. In addition,
first primary column 910 and second primary column 920 may refresh at
different rates, or have different display rules. In one application,
important news such as world headlines ("Big News") appears on the second
primary column 920, while specific or categorized content appears on the
first primary column 910. The Big News may be more important, and require
less updating, as such news has long news cycles. On the other hand,
category specific news may be refreshed more quickly, so that repeat
visits to the page 900 is more likely to ensure fresh content for the
viewer.

[0120] One manner in which the category specific web-pages may be provided
to a user is through use of a search function. The search function may
act as a prompt. A user may enter a search term, such as for example, a
celebrity name, or the name of a disease. The search term may correspond
to a web page displaying category-specific content. The search result may
be the formatted web-page corresponding to the search result. That page
may display updated content that is specific to the category of the
search term.

[0121] Search Specific Categories

[0122] In an embodiment, categories may be generated, or re-configured
from existing categories, based on information entered by or determined
from a user. FIG. 10 illustrates an embodiment in which a category page
of content items may be generated or configured based on such
information. One specific type of information that may be used to
generate such a page is geographic location specified by the user. For
example, a user may utilize a service such as described in FIG. 10 to
research or review content (e.g. local news) about the user's home
destination, or an intended vacation destination.

[0123] In step 1010, content items located by crawler 110 are associated
with geographic information. This step may be done on an ongoing basis
with the aggregation of the content items. The search information may be
selected so as to enable subsequent retrieval of content items responsive
to user information that matches the search information. Examples of
geographic information that can be stored for each content item include
longitude, latitude, and/or zip code. The content items may be scanned
for geographic information, using techniques such as described above, in
order to associate the content items with a specific geographic
information item. For example, a location of a source of the content item
and/or of the subject of the content item may be identified and
associated with that content item.

[0124] In step 1020, geographic information for use in a search is
received from the user. In the example provided above, the information
may correspond to known geographic or location information about the
user. For example, the user may enter his zip code, or exhibit actions
indicating the user's geographic location. The geographic information may
correspond to the longitude, latitude, street address, city or zip code
of the user. The information may be determined from the user, either
directly or indirectly. For example, the user's terminal may include
cookies that identify the user's zip code or location. Alternatively, the
information may be entered by the user as input, such as through a search
interface.

[0125] In step 1030, content items are selected for the user based on the
geographic information specified by the user. While embodiments described
above provide for displaying categories to the user based on the search
term, another embodiment may provide for reconfiguring one or more
categories that match the search result to be location specific. Still
further, embodiment provides for identifying on-the-fly a set of content
items based on the geographic information specified by the user.

[0126] Responsive to receiving the geographic information item, step 1040
provides that the selected content items are presented to the user. In
one embodiment, selected content items are sorted by an approximate
distance from the user. For example, for cases when content items
correspond to news, news stories in the user's town are prominently
displayed, while news stories in an adjacent metropolis or the user's
state or less prominently displayed. Still further, the order in which
the news stories are presented to the user may be based on a distance of
the geographic location stored with the particular news story and the
location detected for the user.

[0127] To provide an example of an embodiment such as described in FIG.
10, user-input, past online activities (as tracked by cookies or other
data) may be used to determine a location of the user. The location may
be determined as longitude and latitude. When the user enters geographic
input, content items are identified that match the user's location. This
may include content items that are determined to be sufficiently
proximate to the user (e.g. within 50 miles or in the same county). These
content items are then included or otherwise provided for in a page or
presentation displayed to the user.

[0128] As another example, the user may enter a zip code corresponding to
his suburb. In this example, content items may be selected which match
the zip code, and which match surrounding suburbs as well as the major
metropolis of the locality. The page presented to the user may be
configured to show the news stories (or other content items) of that
person's specific suburb first. The remainder of the selected content
items may be presented based on a distance of the subject or location of
the content item from the user. For example, news stories of adjacent
and/or most proximate suburbs may be displayed first, followed by the
metropolis region, which may be further away than the surrounding
suburbs. Thus the order of presentation for a list of content items
provided on a page may be determined by the distance of the locations of
those content items (e.g. subject or location of news story) from the
known location of the user.

[0129] Editorial Class User Collaboration and Input

[0130] While embodiments described with, for example, FIG. 1 thru FIG. 6
provide for use of generating formatted pages that display
programmatically categorized and selected content
("programmatically-identified content") from various network locations,
other embodiments may provide for the creation of formatted pages or
presentations that include pages on which programmatically
selected/categorized content and manually-identified content from website
users may be combined and displayed. Both the programmatically identified
content and the manually-identified content may originate from different
domains and/or network locations. Formatted pages or presentations may be
assigned categories, topics or other class identifiers, so that the
combination of programmatically identified content and user-selected
content pertain to, for example, a common category. According to one
embodiment, the use of programmatically identified content is provided to
supplement select pages or presentations in the absence of
user-identified content.

[0131]FIG. 11 illustrates a system for providing categorized content that
is both programmatically and user-identified on a page or presentation of
a website, according to one or more embodiments of the invention. In an
embodiment, a system 1100 includes a content engine 1110, a data store
1120, a presentation component 1130, an editorial class user-interface
1140, and an approval review process 1150.

[0132] Content engine 1110 may execute to programmatically identify and
post content items to specific pages 1152 that are generated from the
presentation component 1130. The presentation component 1130 may display
pages 1152 (or other presentations) for users of the site. As mentioned
with one or more other embodiments, pages 1152 may be categorized or
associated with topics regarding the subject matter of the content that
is displayed.

[0133] According to an embodiment, the content engine 1110 may operate to
programmatically retrieve content items 1108 from different network
locations, and to analyze the retrieved content in order to identify one
or more categories or topical assignments for the content item. With
reference to an embodiment of FIG. 1, content engine 1110 may operate by
the combination of the crawler 110, the categorizer 120, and the
knowledge database 130. The data store 1120 may operate, under one
embodiment, in a manner described with the bucket 140. Either content
engine 1110 or presentation component 1130 may perform function of editor
150, in which content items are selected for given pages 1152. The
presentation component 1120 may further perform operations of, for
example, the display process 630 (see FIG. 6).

[0134] In operation, content engine 1110 analyzes retrieved content items
and outputs data 1112 that associates individual content items 1108 with
one of the categories 1109 that are provided at the site. The data 1112
may include an identifier of the content item 1108, a link to the source
of the content item, and/or text or other content from the content item.
After analyses, attributes or characteristics of the data 1108 are stored
in the data store 1120, so that an association is maintained between
identified categories 1109 and the corresponding content items 1108.

[0135] As mentioned, content items 1108 retrieved and categorized by the
content engine 1110 may correspond to programmatically identified
content. Web pages 1152 may be assigned to individual categories to
display (selectively or otherwise) categorized content stored in the data
store 1120. In one embodiment, the programmatic identification of content
items 1108 for individual web pages 1152 is performed in combination with
a manual identification and selection process for the same page.

[0136] In an embodiment, site on which system 1100 operates may enroll a
class of editor/users ("editorial class users"). Editorial class users
may be regular users of the site, who also serve an additional role of
identifying content items for use on the site. The selection and
publication of news items or blog entries, for example, is a type of
user-activity that has become popular on the Internet. Individuals who
can publish news stories or other content may publicize their opinions
and partake in current events. In one embodiment, the editorial class
users may perform tasks that include selecting content items (e.g. news
stories, pictures, blog entries), making changes or editorial commentary,
and submitting the content items. The site may implement rules and
editorial discretion as to the limits of edits and variations that can be
made to the content items by the editorial class users. Content items
submitted from a member of the editorial class may be displayed
prominently, with the same, similar or greater level of prominence as
programmatically identified items.

[0137] Members 1151 of the editorial class of users may differ from both
users and operators of the site. As editorial class, users may have at
least some of the following characteristics: (i) they use the site on
which system 1100 executes for enjoyment or interest, (ii) their
interaction with the site is through the Internet, and particularly
through the website as externally recognized users, and/or (iii) they
operate or perform without control or direction from the site operator
(but content items submitted from them may be approved or edited).
According to an embodiment, the members 1151 have rights to submit
content items only for pages of a select category or topic. They may also
need to be approved before becoming part of the class through an approval
process. Additionally, they be removed from the class at the discretion
of the site operator.

[0138] In an embodiment, members 1151 interact with the class interface
1150 in order to make submissions, either with or without variations
and/or editorializations. In one embodiment, class interface 1150
corresponds to an input mechanisms 1152 that is provided in the
presentation of the pages 1152. As such, one or more embodiments provide
that the class interface 1150 is part of the presentation component 1130,
which serves as the user-interface for generating web pages 1152. For
example, some or all of the categorized web pages 1152 may include a
login and password feature that enables the member 1151 to login. Upon
login, the editor may be provided a text box and other input fields by
which he may specify or select content items for submission. Under one
implementation, editor 1151 may operate a web browser in navigating to
the site (through the domain), logging in and making the submission.
Input from the editor 1151 may be received as editor class input 1151.
Editor class input may include one or more of: (i) composed message from
editor 1151, (ii) identification of a selected content item, such as by
URL, (iii) proposed changes or edits to the content item, (iv) a picture
or other media (selected specifically by the editor 1151 or provided as
part of the content item), and (v) a quotation selection from the content
item or otherwise.

[0139] According to an embodiment, the presentation component 1130 may
communicate the editor class input 1155 as submission content 1132 to the
approval process 1140. The submission content 1132 may be stored in
folders or held for further review. In one embodiment, approval process
1140 is manual. Operators of the site (those under control of site) may
review the submission content 1132 and perform actions corresponding to
one of (i) reject the content, (ii) approve the content, or (iii) edit,
un-edit edits of the editor 1151, or otherwise make variations to what
was submitted.

[0140] The submission content 1132 may be designated to one of the
categories, based on the identity of the editor 1151, or based on other
factors (e.g. designation made by the editor 1151, or the page from which
the editor logged in). Once approved, the submission content 1132 is
stored as approved content 1156 with the data store 1120. A category
identification may be associated and stored with the approved content
1156.

[0141] The presentation component 1130 may perform retrieval and rendering
operations to display content and content items identified or otherwise
provided by the data store 1120. In an embodiment, the content retrieved
includes the programmatically identified content and the approved content
1156. The retrieval from the data store 1120 may be selective to
categories being displayed.

[0142] One or more embodiments provide that a prioritization or selection
scheme may be implemented by the presentation component 1130 in order to
determine (i) whether to use programmatically identified content items,
or editor class content (e.g. approved content 1156). The prioritization
or selection schemes may consider percentage of each kind of content
items, freshness of the respective type of content items, and whether
there is sufficient diversity in the range of material or class
(editorial or programmatic) in use.

[0143] According to one embodiment, the following algorithm may be used in
determining whether editorial class content or programmatically
identified content should be used, at least for a portion or section of a
page or class of pages: (i) maintain running average time between
editorial class content displayed on a given page or cluster of pages
(associated by category or otherwise), (ii) if time elapsed from last
editorial class input exceeds a threshold that exceeds the average
running time, insert programmatically identified content item. Under one
implementation, for example, the time elapsed from the last editorial
class input must exceed four times the average lapse as identified by the
running average.

[0144]FIG. 12 illustrates a process for enabling editorial class selected
content to be displayed in connection with programmatically identified
content, under an embodiment of the invention. A process such as
illustrated with FIG. 12 may be performed using some or all of the
components illustrated with, for example, an embodiment of FIG. 11.
Accordingly, reference may be made to elements of FIG. 11, or elsewhere,
for purpose of illustrating a suitable element, component or module for
performing a step or sub-step being described.

[0145] In FIG. 12, an overall process for enabling editorial class
selected content to be displayed with programmatically identified content
includes (i) a setup process 1210, (ii) an editorial class submission
process 1220, and (iii) a supplemental process 1230. The setup process
120 may include a step 1212 for enabling users of a general class to
register or apply as editorial class users. As part of step 1212, for
example, the users may provide information about their qualifications,
their samples or interests, a biography, and the particular category for
which they want to be involved with. Step 1214 provides that the site
operator approves (or disapproves) the application or registration of the
user. As mentioned, one or more embodiments provide that the site
operator may approve and then disapprove or remove editors for various
reasons. Among them, the editorial class users may be rated on a variety
of factors, including input from users of the general class, from the
number of submitted items that are rejected and other feedback.

[0146] The editorial class submission process 1220 may include step 1232,
in which the editorial class user submits the content item. The
submission may identify or include one or more of the following: (i) a
link to the content item, (ii) a title or heading of the content item,
(iii) a summary or other text contained in the content item, (iv) a
picture, and/or a (v) a quote for display in connection with the
submitted item. In addition, one or more embodiments provide that the
editorial class user may alter, edit, select or make variations to any of
the above. For example, the picture or quotation may be selected or
submitted apart from the content item (e.g. it may exist with a different
source). Additionally, the submitted item may include text created by the
editor, such as an editorial or commentary.

[0147] In step 1234 of the submission process 1230, the site operator may
approve, reject or further edit the submitted content item. For example,
the site operator may reject submitted content if it contains
objectionable material or duplicates content provided by another user of
the editorial class for the same page or category. Step 1236 provides
that the approved content is then posted. The submitted content may be
posted shortly after submission, pending, for example, manual approval
process. However, one or more embodiments contemplate use of programmatic
approval processes for performing step 1232. In such cases, the postings
may be made to a corresponding page almost immediately.

[0148] The supplement process 1250 provides for programmatically
identified content items to be used as supplements on categorical pages
in which editorial class users are submitting content. Such as embodiment
may assume prioritization is provided to the submitted content of the
editorial class users, although the preference and priority is one of
design by the operator.

[0149] Step 1252 provides for programmatic monitoring of content
submissions on a given page or cluster of pages (which may relate to a
topic or category). In one embodiment, the monitoring is to identify the
span between content submissions from the editorial class user. The
monitoring may track (i) average time between submissions, and (ii)
detect when there is a lapse between submissions that is sufficiently
long to monitor the use of a programmatically identified content item.
Based on the tracking, step 1254 may include a determination as to
whether a need is triggered for inclusion of a programmatically
identified content item. If the determination is affirmative, step 1258
provides that the programmatically identified content is submitted.

[0150]FIG. 13 illustrates a tool for facilitating an editorial class of
users in editing content items for submission on a site, under an
embodiment of the invention. In an embodiment, the tool may be provided
online, such as through programming or script executing (in part) with
the presentation component 1130 (FIG. 11). The tool may include a
user-interface component 1310, a retrieval component 1320, and a text
parser 1330. The user-interface 1310 may display one or more fields to
the user. The fields may be capable of receiving text input, file
attachment, link specifications and other forms of online input.

[0151] In one embodiment, the user-interface 1310 receives an input 1306
correspond to a link that designates a network location on another site
or domain, for example, where a content item selected by the editorial
class user is located. User-interface 1310 may submit the link 1312 to a
retrieval component 1320 which access the network location and retrieves
the web page or other file or content provided at the location. A content
1322 from that network location may be parsed by the parsing component
1330 for text. Text data 1332 may be returned to the user-interface 1310
where it is displayed.

[0152] The user-interface 1310 may include functionality to display the
text data 1332 as editable text 1308. For example, the text data 1332 may
be presented in a field that can receive and respond to character entry
and formatting input of the editorial class user. This allows the
editorial class user to perform editing actions, including removing or
adding text, specifying formatting, and inserting comments or remarks.
One or more embodiments provide that these actions may be performed in
addition to enabling the user to attach files or insert pictures. Still
further, the tool may be used by the editor to specify a quotation to be
displayed in prominence with the editorialized and submitted content.

[0153] Once the editorial class user completes the editing actions, that
user may submit the content as modified. In one implementation, for
example, the tool enables the user to perform actions that include
modifying the headline to a news item, to select an abstract from the
news item, and to delete or redact portions of the news item.

[0154]FIG. 14 illustrates a presentation provided by a user-interface of
a tool such as described with an embodiment of FIG. 13. In an embodiment,
a display 1410 is generated for the editorial class user through a
browser window of that user's terminal. Thus, under one implementation,
display 1410 may correspond to web content, such as a form. The display
1410 may include multiple fields in which the user can enter text or make
specifications or selections. The fields may include a window 1402 for
enabling the user to specify the URL or network location of the content
item that is of interest. In one embodiment, the user may copy and paste
the URL over to the field 1402. In another embodiment, the tool may
include a programmatic component that enables the user to open a browser
window and to navigate or web surf to the desired location. At the
desired location, the user may perform a trigger or iconic action, where
the URL of the desired location is automatically copied over for use by
the tool.

[0155] In addition to the field 1402, a text field 1408 may be provided
where the user can alter text content from the article. Formatting may or
may not be carried over. In an implementation such as shown, the
editorial class user has inserted added text 1409 in connection with the
headline 1408. The added text 1409 is delineated, for illustrative
purposes, with symbols "<" and ">". According to various
embodiments, numerous other editorial actions may be performed through
use of various fields and other functionality that can be provided
through the tool and the user-interface 1310.

[0156]FIG. 15 illustrates a network page of content, containing both
editorial class user submitted content and programmatically identified
content, according to an embodiment such as described with FIG. 11 thru
FIG. 14. FIG. 15 may correspond to a web page 1510 that displays
aggravated content that is assigned to a particular category or topic.
Content items may be represented on the page in the form of a plurality
of links 1508, 1509. The links 1508, 1509 may correspond to URLs to news
stories, blog entries, messages, and other underlying content items at
various different network locations.

CONCLUSION

[0157] In the foregoing specification, the invention has been described
with reference to specific embodiments thereof. It will, however, be
evident that various modifications and changes may be made thereto
without departing from the broader spirit and scope of the invention. The
specification and drawings are, accordingly, to be regarded in an
illustrative rather than a restrictive sense.